Discovery of the Tyrobetaine Natural Products and Their Biosynthetic Gene Cluster via Metabologenomics

Activation of cryptic biosynthetic pathways in Saccharopolyspora spinosa through deletion of the spinosyn gene cluster: induction of cryptic and bioactive natural products

Saccharopolyspora spinosa, a member of the Pseudonocardiaceae family, was originally isolated from soil in the Virgin Islands and is renowned for producing spinosad, a broad-spectrum insecticidal secondary metabolite. While research on S. spinosa has historically focused on spinosad production, little is known about the broader spectrum of secondary metabolites encoded by its genome. Like Streptomyces, S. spinosa harbors numerous biosynthetic gene clusters (BGCs), many of which remain cryptic under standard laboratory conditions. In this study, the spinosyn gene cluster was deleted using the heat-sensitive vector pKC1139, generating the mutant strain S. spinosaΔSPN. The fermentation products of both the wild-type S. spinosa (B1) and S. spinosaΔSPN (B2) were analyzed through HPLC coupled with high-resolution tandem mass spectrometry (HRMS/MS). Data analysis was conducted using GNPS-based molecular networking and MestReNova. A total of seven metabolites were putatively annotated in the wild-type strain (B1), with spinosyns being the predominant compounds. In contrast, the mutant strain (B2) produced putatively linear and cyclic lipopeptides, including gageostatins and gageopeptins as the major metabolites. Additionally, the crude extract from S. spinosaΔSPN (B2) exhibited antibacterial activity, likely due to the production of lipopeptides, which are known for their antimicrobial properties. These findings indicate that deletion of the spinosyn gene cluster can activate cryptic biosynthetic pathways, leading to the discovery of novel bioactive compounds with potential applications in medicine.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Oxytetracycline hyper-production through targeted genome reduction of Streptomyces rimosus

Most biosynthetic gene clusters (BGC) encoding the synthesis of important microbial secondary metabolites, such as antibiotics, are either silent or poorly expressed; therefore, to ensure a strong pipeline of novel antibiotics, there is a need to develop rapid and efficient strain development approaches. This study uses comparative genome analysis to instruct rational strain improvement, using Streptomyces rimosus, the producer of the important antibiotic oxytetracycline (OTC) as a model system. Sequencing of the genomes of two industrial strains M4018 and R6-500, developed independently from a common ancestor, identified large DNA rearrangements located at the chromosome end. We evaluated the effect of these genome deletions on the parental S. rimosus Type Strain (ATCC 10970) genome where introduction of a 145 kb deletion close to the OTC BGC in the Type Strain resulted in massive OTC overproduction, achieving titers that were equivalent to M4018 and R6-500. Transcriptome data supported the hypothesis that the reason for such an increase in OTC biosynthesis was due to enhanced transcription of the OTC BGC and not due to enhanced substrate supply. We also observed changes in the expression of other cryptic BGCs; some metabolites, undetectable in ATCC 10970, were now produced at high titers. This study demonstrated for the first time that the main force behind BGC overexpression is genome rearrangement. This new approach demonstrates great potential to activate cryptic gene clusters of yet unexplored natural products of medical and industrial value.IMPORTANCEThere is a critical need to develop novel antibiotics to combat antimicrobial resistance. Streptomyces species are very rich source of antibiotics, typically encoding 20-60 biosynthetic gene clusters (BGCs). However, under laboratory conditions, most are either silent or poorly expressed so that their products are only detectable at nanogram quantities, which hampers drug development efforts. To address this subject, we used comparative genome analysis of industrial Streptomyces rimosus strains producing high titers of a broad spectrum antibiotic oxytetracycline (OTC), developed during decades of industrial strain improvement. Interestingly, large-scale chromosomal deletions were observed. Based on this information, we carried out targeted genome deletions in the native strain S. rimosus ATCC 10970, and we show that a targeted deletion in the vicinity of the OTC BGC significantly induced expression of the OTC BGC, as well as some other silent BGCs, thus suggesting that this approach may be a useful way to identify new natural products.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Biosynthetic gene profiling and genomic potential of the novel photosynthetic marine bacterium Roseibaca domitiana

Shifting the bioprospecting targets toward underexplored bacterial groups combined with genome mining studies contributes to avoiding the rediscovery of known compounds by revealing novel, promising biosynthetic gene clusters (BGCs). With the aim of determining the biosynthetic potential of a novel marine bacterium, strain V10T, isolated from the Domitian littoral in Italy, a comparative phylogenomic mining study was performed across related photosynthetic bacterial groups from an evolutionary perspective. Studies on polyphasic and taxogenomics showed that this bacterium constitutes a new species, designated Roseibaca domitiana sp. nov. To date, this genus has only one other validly described species, which was isolated from a hypersaline Antarctic lake. The genomic evolutionary study linked to BGC diversity revealed that there is a close relationship between the phylogenetic distance of the members of the photosynthetic genera Roseibaca, Roseinatronobacter, and Rhodobaca and their BGC profiles, whose conservation pattern allows discriminating between these genera. On the contrary, the rest of the species related to Roseibaca domitiana exhibited an individual species pattern unrelated to genome size or source of isolation. This study showed that photosynthetic strains possess a streamlined content of BGCs, of which 94.34% of the clusters with biotechnological interest (NRPS, PKS, RRE, and RiPP) are completely new. Among these stand out T1PKS, exclusive of R. domitiana V10T, and RRE, highly conserved only in R. domitiana V10T and R. ekhonensis, both categories of BGCs involved in the synthesis of plant growth-promoting compounds and antitumoral compounds, respectively. In all cases, with very low homology with already patented molecules. Our findings reveal the high biosynthetic potential of infrequently cultured bacterial groups, suggesting the need to redirect attention to microbial minorities as a novel and vast source of bioactive compounds still to be exploited.

Integrated Metabolomic and Transcriptomic Analysis of the Quinoa Seedling Response to High Relative Humidity Stress

Quinoa is of great interest because it is cold- and drought-resistant; however, little research has been performed on quinoa under high relative humidity (RH) stress. In this study, quinoa seedlings of a highly HR-resistant variety ("Dianli-439") and a sensitive variety ("Dianli-969") were subjected to morphological and physiological measurements and metabolome and transcriptome analyses to investigate their response to high RH stress. In total, 1060 metabolites were detected, and lipids and flavonoids were the most abundant, with 173 and 167 metabolites, respectively. In total, 13,095 differentially expressed genes were identified, and the results showed that abscisic acid, auxin, and jasmonic-acid-related genes involved in plant hormone signaling may be involved in the response of quinoa seedlings to high RH stress. The analysis of the transcription factors revealed that the AP2/ERF family may also play an important role in the response to high RH stress. We identified the possible regulatory mechanisms of the hormone signaling pathways under high RH stress. Our findings can provide a basis for the selection and identification of highly resistant quinoa varieties and the screening of the metabolite-synthesis- and gene-regulation-related mechanisms in quinoa in response to RH stress.

Investigation of diverse biosynthetic secondary metabolites gene clusters using genome mining of indigenous Streptomyces strains isolated from saline soils in Iran

Background and Objectives

Bioactive secondary metabolites are the products of microbial communities adapting to environmental challenges, which have yet remained anonymous. As a result of demands in the pharmaceutical, agricultural, and food industries, microbial metabolites should be investigated. The most substantial sources of secondary metabolites are Streptomyces strains and are potential candidates for bioactive compound production. So, we used genome mining and bioinformatics to predict the isolates secondary metabolites, biosynthesis, and potential pharmaceuticals.

Materials and Methods

This is a bioinformatics part of our previous experimental research. Here, we aimed to inspect the underlying secondary metabolite properties of 20 phylogenetically diverse Streptomyces species of saline soil by a rationalized computational workflow by several software tools. We examined the Metabolites' cytotoxicity and antibacterial effects using the MTT assay and plate count technique, respectively.

Results

Among Streptomyces species, three were selected for genome mining and predicted novel secondary metabolites and potential drug abilities. All 11 metabolites were cytotoxic to A549, but ectoine (p≤0.5) and geosmin (p≤0.001) significantly operated as an anti-cancer drug. Metabolites of oxytetracycline and phosphinothricin (p≤0.001), 4Z-annimycin and geosmin (p≤0.01), and ectoine (p≤0.5) revealed significant antibacterial activity.

Conclusion

Of all the 11 compounds investigated, annimycin, geosmin, phosphinothricin, and ectoine had antimicrobial properties, but geosmin also showed very significant anti-cancer properties.

Momomycin, an Antiproliferative Cryptic Metabolite from the Oxytetracycline Producer Streptomyces rimosus

Natural products provide an important source of pharmaceuticals and chemical tools. Traditionally, assessment of unexplored microbial phyla has led to new natural products. However, with every new microbe, the number of orphan biosynthetic gene clusters (BGC) grows. As such, the more difficult proposition is finding new molecules from well-studied strains. Herein, we targeted Streptomyces rimosus, the widely-used oxytetracycline producer, for the discovery of new natural products. Using MALDI-MS-guided high-throughput elicitor screening (HiTES), we mapped the global secondary metabolome of S. rimosus and structurally characterized products of three cryptic BGCs, including momomycin, an unusual cyclic peptide natural product with backbone modifications and several non-canonical amino acids. We elucidated important aspects of its biosynthesis and evaluated its bioactivity. Our studies showcase HiTES as an effective approach for unearthing new chemical matter from "drained" strains.

Chemometrics and genome mining reveal an unprecedented family of sugar acid-containing fungal nonribosomal cyclodepsipeptides

Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)-based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d-xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d-xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N-methylated amino acid residues in positions where the corresponding NRPS modules lack N-methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.

Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products

The discovery of novel antimicrobials has significantly slowed down over the last three decades. At the same time, humans rely increasingly on antimicrobials because of the progressive antimicrobial resistance in medical practices, human communities, and the environment. Data mining is currently considered a promising option in the discovery of new antibiotics. Some of the advantages of data mining are the ability to predict chemical structures from sequence data, anticipation of the presence of novel metabolites, the understanding of gene evolution, and the corroboration of data from multiple omics technologies. This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics. It also summarizes some of the most recent research accomplishments in the field, all pinpointing to innovation through uncovering and implementing the next generation of antimicrobials.

Transcriptomic and Metabolomic Analysis of the Response of Quinoa Seedlings to Low Temperatures

Quinoa, a cool-weather high-altitude crop, is susceptible to low-temperature stress throughout its reproductive phase. Herein, we performed broadly targeted metabolic profiling of quinoa seedlings to explore the metabolites’ dynamics in response to low-temperature stress and transcriptome analysis to determine the underlying genetic mechanisms. Two variants, namely, Dian Quinoa 2324 and Dian Quinoa 281, were exposed to temperatures of −2, 5, and 22 °C. A total of 794 metabolites were detected; 52,845 genes, including 6628 novel genes, were annotated using UPLC-MS/MS analysis and the Illumina HiSeq system. Combined with morphological indicators to resolve the mechanism underlying quinoa seedling response to low-temperature stress, the molecular mechanisms of quinoa changed considerably based on temperature exposure. Soluble sugars heavily accumulated in plants with cold damage and changes in regulatory networks under freeze damage, such as the upregulation of α-linolenic acid metabolism and a reduction in energy substrates, may explain the spatial patterns of biosynthesis and accumulation of these metabolites. Genes that are actively expressed during cold responses, as revealed by co-expression analyses, may be involved in the regulation thereof. These results provide insights into the metabolic factors in quinoa under low-temperature stress and provide a reference for the screening of quinoa varieties resistant to low temperature.

Comprehensive genome analysis of Lentzea reveals repertoire of polymer-degrading enzymes and bioactive compounds with clinical relevance

The genus Lentzea is a rare group of actinobacteria having potential for the exploration of bioactive compounds. Despite its proven ability to produce compounds with medical relevance, Lentzea genome analysis remains unexplored. Here we show a detailed understanding of the genetic features, biosynthetic gene clusters (BGCs), and genetic clusters for carbohydrate-active enzymes present in the Lentzea genome. Our analysis determines the genes for core proteins, non-ribosomal peptide synthetase condensation domain, and polyketide synthases-ketide synthase domain. The antiSMASH-based sequence analysis identifies 692 BGCs among which 8% are identical to the BGCs that produce geosmin, citrulassin, achromosin (lassopeptide), vancosamine, anabaenopeptin NZ857/nostamide A, alkylresorcinol, BE-54017, and bezastatin. The remaining BGCs code for advanced category antimicrobials like calcium-dependent, glycosylated, terpenoids, lipopeptides, thiopeptide, lanthipeptide, lassopeptide, lingual antimicrobial peptide and lantibiotics together with antiviral, antibacterial, antifungal, antiparasitic, anticancer agents. About 28% of the BGCs, that codes for bioactive secondary metabolites, are exclusive in Lentzea and could lead to new compound discoveries. We also find 7121 genes that code for carbohydrate-degrading enzymes which could essentially convert a wide range of polymeric carbohydrates. Genome mining of such genus is very much useful to give scientific leads for experimental validation in the discovery of new-generation bioactive molecules of biotechnological importance.

Great diversity of KSα sequences from bat-associated microbiota suggests novel sources of uncharacterized natural products

Polyketide synthases (PKSs) are multidomain enzymes in microorganisms that synthesize complex, bioactive molecules. PKS II systems are iterative, containing only a single representative of each domain: ketosynthase alpha (KS[Formula: see text]), ketosynthase beta and the acyl carrier protein. Any gene encoding for one of these domains is representative of an entire PKS II biosynthetic gene cluster (BGC). Bat skin surfaces represent an extreme environment prolific in Actinobacteria that may constitute a source for bioactive molecule discovery. KS[Formula: see text] sequences were obtained from culturable bacteria from bats in the southwestern United States. From 467 bat bacterial isolates, we detected 215 (46%) had KS[Formula: see text] sequences. Sequencing yielded 210 operational taxonomic units, and phylogenetic placement found 45 (21%) shared <85% homology to characterized metabolites. Additionally, 16 Actinobacteria genomes from the bat microbiome were analyzed for biosynthetic capacity. A range of 69-93% of the BGCs were novel suggesting the bat microbiome may contain valuable uncharacterized natural products. Documenting and characterizing these are important in understanding the susceptibility of bats to emerging infectious diseases, such as white-nose syndrome. Also noteworthy was the relationship between KS [Formula: see text] homology and total BGC novelty within each fully sequenced strain. We propose amplification and detection of KS[Formula: see text] could predict a strain's global biosynthetic capacity.

Reference-Grade Genome and Large Linear Plasmid of Streptomyces rimosus: Pushing the Limits of Nanopore Sequencing

Streptomyces rimosus ATCC 10970 is the parental strain of industrial strains used for the commercial production of the important antibiotic oxytetracycline. As an actinobacterium with a large linear chromosome containing numerous long repeat regions, high GC content, and a single giant linear plasmid (GLP), these genomes are challenging to assemble. Here, we apply a hybrid sequencing approach relying on the combination of short- and long-read next-generation sequencing platforms and whole-genome restriction analysis by using pulsed-field gel electrophoresis (PFGE) to produce a high-quality reference genome for this biotechnologically important bacterium. By using PFGE to separate and isolate plasmid DNA from chromosomal DNA, we successfully sequenced the GLP using Nanopore data alone. Using this approach, we compared the sequence of GLP in the parent strain ATCC 10970 with those found in two semi-industrial progenitor strains, R6-500 and M4018. Sequencing of the GLP of these three S. rimosus strains shed light on several rearrangements accompanied by transposase genes, suggesting that transposases play an important role in plasmid and genome plasticity in S. rimosus. The polished annotation of secondary metabolite biosynthetic pathways compared to metabolite analysis in the ATCC 10970 strain also refined our knowledge of the secondary metabolite arsenal of these strains. The proposed methodology is highly applicable to a variety of sequencing projects, as evidenced by the reliable assemblies obtained. IMPORTANCE The genomes of Streptomyces species are difficult to assemble due to long repeats, extrachromosomal elements (giant linear plasmids [GLPs]), rearrangements, and high GC content. To improve the quality of the S. rimosus ATCC 10970 genome, producer of oxytetracycline, we validated the assembly of GLPs by applying a new approach to combine pulsed-field gel electrophoresis separation and GLP isolation and sequenced the isolated GLP with Oxford Nanopore technology. By examining the sequenced plasmids of ATCC 10970 and two industrial progenitor strains, R6-500 and M4018, we identified large GLP rearrangements. Analysis of the assembled plasmid sequences shed light on the role of transposases in genome plasticity of this species. The new methodological approach developed for Nanopore sequencing is highly applicable to a variety of sequencing projects. In addition, we present the annotated reference genome sequence of ATCC 10970 with a detailed analysis of the biosynthetic gene clusters.

Gene editing enables rapid engineering of complex antibiotic assembly lines

Re-engineering biosynthetic assembly lines, including nonribosomal peptide synthetases (NRPS) and related megasynthase enzymes, is a powerful route to new antibiotics and other bioactive natural products that are too complex for chemical synthesis. However, engineering megasynthases is very challenging using current methods. Here, we describe how CRISPR-Cas9 gene editing can be exploited to rapidly engineer one of the most complex megasynthase assembly lines in nature, the 2.0 MDa NRPS enzymes that deliver the lipopeptide antibiotic enduracidin. Gene editing was used to exchange subdomains within the NRPS, altering substrate selectivity, leading to ten new lipopeptide variants in good yields. In contrast, attempts to engineer the same NRPS using a conventional homologous recombination-mediated gene knockout and complementation approach resulted in only traces of new enduracidin variants. In addition to exchanging subdomains within the enduracidin NRPS, subdomains from a range of NRPS enzymes of diverse bacterial origins were also successfully utilized.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

Thirtieth Anniversary of the Discovery of Laxaphycins. Intriguing Peptides Keeping a Part of Their Mystery

Lipopeptides are a class of compounds generally produced by microorganisms through hybrid biosynthetic pathways involving non-ribosomal peptide synthase and a polyketyl synthase. Cyanobacterial-produced laxaphycins are examples of this family of compounds that have expanded over the past three decades. These compounds benefit from technological advances helping in their synthesis and characterization, as well as in deciphering their biosynthesis. The present article attempts to summarize most of the articles that have been published on laxaphycins. The current knowledge on the ecological role of these complex sets of compounds will also be examined.

Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.

Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

Genome Mining and Metabolomics Uncover a Rare d-Capreomycidine Containing Natural Product and Its Biosynthetic Gene Cluster

We report the metabolomics-driven genome mining of a new cyclic-guanidino incorporating non-ribosomal peptide synthetase (NRPS) gene cluster and full structure elucidation of its associated hexapeptide product, faulknamycin. Structural studies unveiled that this natural product contained the previously unknown (R,S)-stereoisomer of capreomycidine, d-capreomycidine. Furthermore, heterologous expression of the identified gene cluster successfully reproduces faulknamycin production without an observed homologue of VioD, the pyridoxal phosphate (PLP)-dependent enzyme found in all previous l-capreomycidine biosynthesis. An alternative NRPS-dependent pathway for d-capreomycidine biosynthesis is proposed.

Acyltransferase that catalyses the condensation of polyketide and peptide moieties of goadvionin hybrid lipopeptides

Fusions of fatty acids and peptides expand the structural diversity of natural products; however, polyketide/ribosomally synthesized and post-translationally modified peptides (PK/RiPPs) hybrid lipopeptides are relatively rare. Here we report a family of PK/RiPPs called goadvionins, which inhibit the growth of Gram-positive bacteria, and an acyltransferase, GdvG, which catalyses the condensation of the PK and RiPP moieties. Goadvionin comprises a trimethylammonio 32-carbon acyl chain and an eight-residue RiPP with an avionin structure. The positions of six hydroxyl groups and one double bond in the very-long acyl chain were determined by radical-induced dissociation tandem mass spectrometry, which collides radical ion species to generate C-C bond cleavage fragments. GdvG belongs to the Gcn5-related N-acetyltransferase superfamily. Unlike conventional acyltransferases, GdvG transfers a very long acyl chain that is tethered to an acyl carrier protein to the N-terminal amino group of the RiPP moiety. gdvG homologues flanked by PK/fatty acid and RiPP biosynthesis genes are widely distributed in microbial species, suggesting that acyltransferase-catalysed condensation of PKs and RiPPs is a general strategy in biosynthesis of similar lipopeptides.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery

Secondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria

Streptomyces bacteria are known for their prolific production of secondary metabolites, many of which have been widely used in human medicine, agriculture and animal health. To guide the effective prioritization of specific biosynthetic gene clusters (BGCs) for drug development and targeting the most prolific producer strains, knowledge about phylogenetic relationships of Streptomyces species, genome-wide diversity and distribution patterns of BGCs is critical. We used genomic and phylogenetic methods to elucidate the diversity of major classes of BGCs in 1,110 publicly available Streptomyces genomes. Genome mining of Streptomyces reveals high diversity of BGCs and variable distribution patterns in the Streptomyces phylogeny, even among very closely related strains. The most common BGCs are non-ribosomal peptide synthetases, type 1 polyketide synthases, terpenes, and lantipeptides. We also found that numerous Streptomyces species harbor BGCs known to encode antitumor compounds. We observed that strains that are considered the same species can vary tremendously in the BGCs they carry, suggesting that strain-level genome sequencing can uncover high levels of BGC diversity and potentially useful derivatives of any one compound. These findings suggest that a strain-level strategy for exploring secondary metabolites for clinical use provides an alternative or complementary approach to discovering novel pharmaceutical compounds from microbes.

A computational framework to explore large-scale biosynthetic diversity

Genome mining has become a key technology to exploit natural product diversity. Although initially performed on a single-genome basis, the process is now being scaled up to mine entire genera, strain collections and microbiomes. However, no bioinformatic framework is currently available for effectively analyzing datasets of this size and complexity. In the present study, a streamlined computational workflow is provided, consisting of two new software tools: the 'biosynthetic gene similarity clustering and prospecting engine' (BiG-SCAPE), which facilitates fast and interactive sequence similarity network analysis of biosynthetic gene clusters and gene cluster families; and the 'core analysis of syntenic orthologues to prioritize natural product gene clusters' (CORASON), which elucidates phylogenetic relationships within and across these families. BiG-SCAPE is validated by correlating its output to metabolomic data across 363 actinobacterial strains and the discovery potential of CORASON is demonstrated by comprehensively mapping biosynthetic diversity across a range of detoxin/rimosamide-related gene cluster families, culminating in the characterization of seven detoxin analogues.

Next-Generation Drug Discovery to Combat Antimicrobial Resistance

The widespread emergence of antibiotic-resistant pathogens poses a severe threat to public health. This problem becomes even worse with a coincident decline in the supply of new antibiotics. Conventional bioactivity-guided natural product discovery has failed to meet the urgent need for new antibiotics, largely due to limited resources and high rediscovery rates. Recent advances in cultivation techniques, analytical technologies, and genomics-based approaches have greatly expanded our access to previously underexploited microbial sources. These strategies will enable us to access new reservoirs of microorganisms and unleash their chemical potentials, thus opening new opportunities for the discovery of next-generation drugs to address the growing concerns of antimicrobial resistance.

Natural products targeting strategies involving molecular networking: different manners, one goal

Covering: up to 2019Landmark advances in bioinformatics tools have recently enhanced the field of natural products research, putting today's natural product chemists in the enviable position of being able to perform the efficient targeting/discovery of previously undescribed molecules by expediting the prioritization of the isolation workflow. Among these advances, MS/MS molecular networking has appeared as a promising approach to dereplicate complex natural product mixtures, leading to a real revolution in the "art of natural product isolation" by accelerating the pace of research of this field. This review illustrates through selected cornerstone studies the new thinking in natural product isolation by drawing a parallel between the different underlying philosophies behind the use of molecular networking in targeting natural products.

Precursor-feeding and altered-growth conditions reveal novel blue pigment production by Rubrivivax benzoatilyticus JA2

OBJECTIVE

To explore the secondary metabolite biosynthetic potential of Rubrivivax benzoatilyticus JA2 using a new metabolite mining strategy.

RESULTS

Combination of precursor-feeding and altered growth conditions were used to mine new biomolecules. Strain JA2 utilised L-phenylalanine as sole source of nitrogen and showed pigments production only under phenylalanine-amended aerobic cultures. Stable isotope based precursor feeding studies indicated the blue pigment consists of 4-phenyl rings derived from L-phenylalanine. The purified blue pigment displayed characteristic visible-absorption and pH-dependent color variations. Precursor-feeding under altered growth conditions activated the plausible novel aromatic pigment production in strain JA2.

CONCLUSION

Our approach unraveled the previously unknown pigment synthesis in strain JA2 and demonstrated the potential of mining strategy in discovering the hidden secondary metabolite repertoire in microorganisms.

Refining and expanding nonribosomal peptide synthetase function and mechanism

Nonribosomal peptide synthetases (NRPSs) are involved in the biosynthesis of numerous peptide and peptide-like natural products that have been exploited in medicine, agriculture, and biotechnology, among other fields. As a consequence, there have been considerable efforts aimed at understanding how NRPSs orchestrate the assembly of these natural products. This review highlights several recent examples that continue to expand upon the fundamental knowledge of NRPS mechanism and includes (1) the discovery of new NRPS substrates and the mechanism by which these sometimes structurally complex substrates are made, (2) the characterization of new NRPS activities and domains that function during the process of peptide assembly, and (3) the various catalytic strategies that are utilized to release the NRPS product. These findings continue to strengthen the predictive power for connecting genes to products, thereby facilitating natural product discovery and development in the Genomics Era.

Hot off the Press

A personal selection of 32 recent papers is presented covering various aspects of current developments in bioorganic chemistry and novel natural products such as mollebenzylanol A from Rhododendron molle.